The Kitchin Research Group

In our recent paper, Multiscale Perturbation Methods for Dynamic/Programmable Catalysis, Carolina Colombo Tedesco, Carl Laird, Aditya Khair, and I developed an analytical framework for modeling dynamic catalysis—where catalyst binding energies are periodically modulated to overcome Sabatier limitations and enhance reaction rates. Building on our previous boundary value problem approach for simulating cyclic steady states, we applied multiscale perturbation theory to decompose the system response into slow (average) and fast (oscillatory) components. This allowed us to derive closed-form expressions for surface coverages and limit cycles without costly numerical integration. The method accurately reproduces results from full simulations for linear systems when the forcing frequency is high and amplitudes are small to moderate, providing insight into how oscillating catalytic systems behave and when they deviate from quasi-static assumptions. Beyond dynamic catalysis, this analytical framework may be useful for understanding other periodically driven systems where time-scale separation enables simple yet powerful approximations.

@article{tedesco-2025-multis-pertur,
  author =       {Carolina Colombo Tedesco and Carl D. Laird and John R. Kitchin
                  and Aditya S. Khair},
  title =        {Multiscale Perturbation Methods for Dynamic/programmable
                  Catalysis},
  journal =      {Industrial \& Engineering Chemistry Research},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.iecr.5c03023},
  year =         2025,
  doi =          {10.1021/acs.iecr.5c03023},
  url =          {http://dx.doi.org/10.1021/acs.iecr.5c03023},
  DATE_ADDED =   {Fri Oct 31 10:39:19 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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In our recent work published in the Journal of the American Chemical Society, we investigated how electrolyte pH affects the oxygen reduction reaction (ORR) - a critical process in fuel cells and batteries. Working with an outstanding team including Jay Bender, Rohan Sanspeur, and colleagues from UT Austin, we tackled a long-standing puzzle: why does changing pH dramatically affect ORR rates on some catalysts (like Au) but barely affect others (like Pt)?

Through careful electrochemical experiments, we measured ORR activity across different metals in both acidic and alkaline conditions. The results were striking - Au catalysts showed dramatically increased activity when moving from acid to base, while Pt remained essentially unchanged. Our kinetic analysis revealed that the rate-determining steps don't actually change with pH, challenging previous explanations.

The breakthrough came from combining these experiments with density functional theory (DFT) calculations. We discovered that electric field effects provide a unifying explanation. When pH increases, the interfacial electric field becomes more negative. This field change strongly stabilizes the key reaction intermediate (*O₂) on weakly binding metals like Au, dramatically lowering activation barriers. On strongly binding metals like Pt, the reaction intermediates are much less sensitive to electric fields, explaining their pH-independent behavior.

This computational insight allowed us to extend our understanding to other metals (Ag, Ir, Ru, Pd), confirming the general principle: pH effects depend on how field-sensitive the rate-determining intermediates are.

Our work demonstrates the power of combining rigorous experimental kinetics with advanced computational modeling. By understanding these fundamental electric field effects, we can now predict and potentially engineer pH-dependent catalytic behavior - opening new avenues for optimizing electrochemical energy conversion devices.

@article{bender-2025-how-elect,
  author = {Jay T. Bender and Rohan Yuri Sanspeur and Nicolas Bueno Ponce and Angel E. Valles and Alyssa K. Uvodich and Delia J. Milliron and John R. Kitchin and Joaquin Resasco},
  title = {How Electrolyte Ph Affects the Oxygen Reduction Reaction},
  journal = {Journal of the American Chemical Society},
  volume = {nil},
  number = {nil},
  pages = {jacs.5c14208},
  year = {2025},
  doi = {10.1021/jacs.5c14208},
  url = {http://dx.doi.org/10.1021/jacs.5c14208},
  DATE_ADDED = {Thu Oct 2 07:22:51 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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Org-mode version = 9.8-pre

In our latest work, we tackled a critical challenge in materials modeling: ensuring that AI models don't just make predictions, but also tell us how confident they are in those predictions. When Graph Neural Networks (GNNs) encounter new, "out-of-domain" materials they haven't seen during training, their predictions can become unreliable, and it's tough to know when that's happening. So, we integrated a clever, lightweight technique called Direct Propagation of Shallow Ensembles (DPOSE) into a GNN model called SchNet. Essentially, DPOSE allows the model to estimate its own uncertainty efficiently. Our findings showed that this approach is really effective at flagging when the model is venturing into unfamiliar territory, giving us higher uncertainty for novel molecules or material structures across various datasets. While it performed well, we also learned about its limitations in distinguishing very subtle structural differences. Ultimately, this work is a step towards building more trustworthy AI for materials discovery, paving the way for smarter active learning strategies where the AI itself helps decide what new data to explore.

@article{vinchurkar-2025-uncer-quant,
  author =       {Tirtha Vinchurkar and Kareem Abdelmaqsoud and John R Kitchin},
  title =        {Uncertainty Quantification in Graph Neural Networks With
                  Shallow Ensembles},
  journal =      {Machine Learning: Science and Technology},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2025,
  doi =          {10.1088/2632-2153/ae0bf0},
  url =          {https://doi.org/10.1088/2632-2153/ae0bf0},
  DATE_ADDED =   {Sat Sep 27 11:42:36 2025},
}

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Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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Org-mode version = 9.8-pre

In our new paper published in Nature Machine Intelligence, my colleagues Hongliang Xin, Heather Kulik, and I explore what we call "agentic science" – a new paradigm where AI agents can (semi-)autonomously conduct scientific research.

Scientific discovery has evolved through distinct eras: from early empirical observations and theoretical frameworks like Newtonian mechanics, through the computational modeling revolution, to today's data science approaches. We argue that we're now entering the age of agentic science, where AI systems don't just analyze data but can independently reason, plan experiments, and interact with both digital tools and physical laboratory equipment.

What makes these AI agents special is their capacity for independent agency. Built around large language models that can process text, images, and structured data, they can actively learn, integrate with external tools, and think strategically about long-term research goals. Systems like Coscientist can already interpret natural language requests and autonomously operate lab equipment, while A-Lab represents a fully autonomous materials synthesis laboratory.

However, we're honest about the challenges. These systems can "hallucinate" – producing convincing but incorrect information – and they're sensitive to how questions are phrased. We also lack standardized ways to evaluate their performance, and they consume significant computational resources.

The key to success lies in maintaining human oversight while leveraging AI for high-throughput tasks. With proper safeguards, transparent documentation, and ethical considerations, agentic AI could dramatically accelerate scientific discovery while actually improving reproducibility by systematically analyzing literature and identifying research gaps.

We believe this represents a fundamental shift in how science gets done – not replacing human scientists, but creating powerful human-AI partnerships that could unlock new pathways to discovery.

@article{xin-2025-towar-agent,
  author =       {Hongliang Xin and John R. Kitchin and Heather J. Kulik},
  title =        {Towards Agentic Science for Advancing Scientific Discovery},
  journal =      {Nature Machine Intelligence},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2025,
  doi =          {10.1038/s42256-025-01110-x},
  url =          {https://doi.org/10.1038/s42256-025-01110-x},
  DATE_ADDED =   {Tue Sep 16 13:36:03 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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In our latest work, we set out to tackle a common challenge in engineering and science: understanding how small uncertainties in inputs, like temperature changes or slight variations in pressure, can ripple through complex systems and affect the final outcome. Instead of relying on slow, trial-and-error simulations, we leveraged an emerging computing technique that treats uncertainty like a path we can follow mathematically. By "teaching" our software to calculate these paths directly, we can predict how errors build up in real processes, whether in a chemical reactor, a filtration system, or a bioreactor, 100 times faster than traditional methods. This speed and precision mean we can design safer, more reliable systems and respond more quickly when things don't go exactly as planned.

We introduce a differentiable-programming-based framework for uncertainty quantification that leverages the implicit function theorem and path integration to compute both forward and inverse uncertainty maps directly from high-fidelity or surrogate models . Our approach requires only C¹ differentiability and injectivity of the implicit system and avoids expensive Monte Carlo sampling by tracing uncertainty "contours" through the model. We validate it on three chemical-engineering case studies: a CSTR (showing exact agreement with analytical solutions in unimodal and multimodal scenarios), a membrane reactor for natural-gas aromatization (recovering 95% of exhaustive-search samples in 7 min vs. ~10 h), and a fed-batch bioreactor with 3D ellipsoidal uncertainty regions. All code and reproducible Jupyter notebooks are available at github.com/KitchinHUB/Mapping-Uncertainty-Using-Differentiable-Programming.

@article{alves-2025-mappin-uncer,
  author =       {Victor Alves and Carl D. Laird and Fernando V. Lima and John
                  R. Kitchin},
  title =        {Mapping Uncertainty Using Differentiable Programming},
  journal =      {AIChE Journal},
  volume =       {},
  number =       {},
  pages =        {e18940},
  year =         2025,
  doi =          {10.1002/aic.18940},
  url =          {https://doi.org/10.1002/aic.18940},
  DATE_ADDED =   {Thu Jul 31 09:53:53 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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Org-mode version = 9.8-pre